Machine Learning Executive Training - Level 3 Advanced
EBS Corp.

Machine Learning Executive Training - Level 3 Advanced

Regular price $10,800.00 $10,800.00

DURATION: 36 HOURS
SCHEDULE: FLEXIBLE

TOPICS COVERED

DAY 1

  • Review the basics of Python and Data Science

DAY 2

  • Supervised vs Unsupervised Learning
  • Regression vs Classification models
  • Categorical vs Continuous feature spaces
  • Interpreting Results of Regression and Classification Models
  • Parameters and Hyper Parameters

DAY 3

  • Modeling Fundamentals: Test-train split, Cross-validation (CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
  • Interpreting Results of Regression and Classification Models (Hands On)
  • Parameters and Hyper Parameters
  • Resampling, Bootstrapping, and Cross-Validation

DAY 4

  • Regression: Linear Regression, Polynomial Regression, Backward & Forward Elimination of Regressors
  • Classification: Naive Bayes, Logistic Regression, Support Vector Machines
  • Regularization: Lasso and Ridge Regression
  • Dimension Reduction Trees: Decision Trees, Bagging, Boosting, Random Forest
  • Unsupervised Learning: K-Means Clustering, Neural Networks: Intro To Artificial Neural Networks and Deep Learning
  • Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python
  • Calculating R-Square, MSE, Logit manually in excel for enhanced understanding (Multiple Regression)

DAY 5

  • Built your own model with Joshi and start with your own Data from Kaggle.
  • Select your project, download data, clean wrangle and massage your data and make it ready for analysis for Titanic, Iris, and other common Data sets.
  • Build your own model with Joshi and start with your own Data from Kaggle.

DAY 6

  • Model your data with the instructor
  • Run Machine Learning Models and select the best model
  • Tweak Model parameters for Titanic Iris Dataset
  • Regression analysis K-Means Clustering Principal Component
  • Analysis Train/Test and cross validation Bayesian Methods
  • Decision Trees and Random Forests Multivariate Regression
  • Multi-Level Models Support Vector Machines K-Nearest Neighbor
  • Bias/Variance Tradeoff Ensemble LearningUnderstanding
  • Calculating R-Square, MSE, Logit manually in excel for enhanced understanding
  • Understanding features of Popular Datasets: Titanic, Iris and Housing Prices
  • Running Logistic Regression on Titanic Data Set
  • Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset
  • Top 20 machine learning interview question
  • Small Project for Github
  • Make data ready, choose and configure the correct model for your data
  • Interpret the results of your machine learning algorithm

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